SFARI Genes Classification Model

library(tidyverse)
library(knitr)
library(plotly) ; library(viridis) ; library(gridExtra) ; library(RColorBrewer) ; library(ggpubr) ; library(rstatix)
library(biomaRt)
library(polycor)
library(caret) ; library(ROCR) ; library(car) ; library(MLmetrics)
library(corrplot)
library(expss) ; library(knitr) ; library(kableExtra)
library(foreach) ; library(doParallel)

SFARI_colour_hue = function(r) {
  pal = c('#FF7631','#FFB100','#E8E328','#8CC83F','#62CCA6','#59B9C9','#b3b3b3','#808080','gray','#d9d9d9')[r]
}

Load Dataset

clustering_selected = 'DynamicHybrid'
clusterings = read_csv('./../Data/clusters.csv')
clusterings$Module = clusterings[,clustering_selected] %>% data.frame %>% unlist %>% unname
assigned_module = clusterings %>% dplyr::select(ID, Module)

# Dataset created with 20_04_07_create_dataset.html
dataset = read.csv(paste0('./../Data/dataset_', clustering_selected, '.csv'), row.names=1)
rownames_dataset = rownames(dataset)
dataset = dataset %>% mutate(Module = clusterings$Module) %>% dplyr::select(-matches(clustering_selected))
rownames(dataset) = rownames_dataset

# Fix any Gene Significance that is NA
GS_missing = rownames(dataset)[is.na(dataset$GS)]
if(length(GS_missing)>0){
  # Gandal dataset
  load('./../Data/preprocessed_data.RData')
  datExpr = datExpr %>% data.frame
  
  for(g in GS_missing){
    dataset$GS[rownames(dataset) == g] = polyserial(as.numeric(datExpr[g,]), datMeta$Diagnosis)
  }
}



# SFARI Genes
SFARI_genes = read_csv('./../../../SFARI/Data/SFARI_genes_01-03-2020_w_ensembl_IDs.csv')
SFARI_genes = SFARI_genes[!duplicated(SFARI_genes$ID) & !is.na(SFARI_genes$ID),]


# GO Neuronal annotations: regex 'neuron' in GO functional annotations and label the genes that make a match as neuronal
GO_annotations = read.csv('./../Data/genes_GO_annotations.csv')
GO_neuronal = GO_annotations %>% filter(grepl('neuron', go_term)) %>% 
              mutate('ID'=as.character(ensembl_gene_id)) %>% 
              dplyr::select(-ensembl_gene_id) %>% distinct(ID) %>%
              mutate('Neuronal'=1)


# Add gene symbol
getinfo = c('ensembl_gene_id','external_gene_id')
mart = useMart(biomart='ENSEMBL_MART_ENSEMBL', dataset='hsapiens_gene_ensembl',
               host='feb2014.archive.ensembl.org') ## Gencode v19
gene_names = getBM(attributes=getinfo, filters=c('ensembl_gene_id'), values=rownames(dataset), mart=mart)


rm(getinfo, mart, rownames_dataset, GO_annotations, g, GS_missing, dds)

The features that will be considered for the classification model will be the ones WGCNA uses to identify significant modules and genes:

• Correlation of a gene’s expression pattern to diagnosis: Using Gene Significance

• Correlation of a gene’s module to Diagnosis: Using Module-Diagnosis correlation

• Module assignment: Here, instead of just indicating the cluster the gene belongs to in a binary way, we decided to use the Module Membership, which measures the similarity of each gene to the module they belong, and we decided to also add the Module Membership to all of the other modules as well, since this could give us much more information than the original binary assignment (using eigengenes)

Filtering the 138 genes that were not assigned to any cluster (represented as the gray cluster)

rm_cluster = dataset[is.na(dataset$MTcor),'Module'] %>% unique %>% as.character

new_dataset = dataset %>% filter(Module != 'gray' & !is.na(MTcor)) %>% 
              dplyr::select(-c(matches(paste('pval|Module')), MMgray)) %>%
              mutate('absGS'=abs(GS), 'SFARI'=gene.score!='Others') %>% dplyr::select(-gene.score)
rownames(new_dataset) = rownames(dataset)[dataset$Module != 'gray']

rm(rm_cluster)

Summary of the changes made to the original WGCNA variables:

• Using Module Membership variables instead of binary module membership

• Including a new variable with the absolute value of GS

• Removing genes assigned to the gray module (unclassified genes)

• Adding the Objective variable: Binary label indicating if it’s in the SFARI dataset or not

table_info = new_dataset %>% head(5) %>% t 

table_info %>% kable(caption = '(Transposed) features and their values for the first rows of dataset', 
                     col.names = colnames(table_info)) %>% kable_styling(full_width = F)

(Transposed) features and their values for the first rows of dataset

	ENSG00000000003	ENSG00000000419	ENSG00000000457	ENSG00000000460	ENSG00000000938
MTcor	0.5650633	0.2024116	-0.8612290	0.1122683	0.9612374
GS	0.3856082	0.0330853	-0.3937957	-0.2418481	0.4196586
MM.00BE6E	0.0922553	0.0349765	-0.0890836	0.2487132	0.0451893
MM.77AF00	0.2199591	0.2824804	-0.2112288	0.0598775	0.2502654
MM.E98428	0.1328112	0.5224834	-0.0918756	0.3717968	-0.1231391
MM.A08CFF	0.2024525	0.0815135	-0.2001277	-0.1452052	0.2712711
MM.00C0B3	0.0815588	0.2369206	-0.1110010	0.1260077	0.2460671
MM.00C1A7	0.2815932	0.0702776	-0.2809204	-0.0886543	0.3920586
MM.4BB400	-0.2319119	-0.0459663	0.2304153	0.2787641	-0.1447272
MM.00ABFD	-0.1179976	0.0221085	0.0641125	0.1968199	0.1703302
MM.C39A00	-0.1073985	0.0985058	0.0051635	0.2543768	0.0234781
MM.44A0FF	0.0725256	0.0210242	-0.1861562	-0.0363138	0.2709805
MM.96A900	-0.1184551	-0.2201542	0.0072814	-0.0657402	0.1906476
MM.F47B5B	-0.0698187	0.3021432	-0.0195570	0.2424089	-0.0914334
MM.00BF7D	0.0052768	0.1737149	-0.0730145	0.0143253	-0.1362776
MM.EF67EB	0.1979762	-0.0057964	-0.1415817	-0.1884690	0.0441187
MM.FB61D8	0.2532797	0.2596606	-0.1685126	0.1603773	-0.0682160
MM.FF66A9	0.3289569	-0.0777291	-0.3149048	-0.3983808	0.2569934
MM.00B0F6	0.0938223	-0.0335679	-0.2462794	0.0789581	0.0741229
MM.64B200	-0.0556433	0.0215751	-0.0760139	-0.0474915	0.3149536
MM.00C19A	0.1278939	-0.0238796	-0.3257874	-0.2659576	0.1003769
MM.6C9AFF	0.1875213	0.2571035	-0.3189170	-0.2042838	0.2097051
MM.88AC00	0.3083110	0.0657020	-0.3673810	-0.2789542	0.3482581
MM.FE61CD	0.2735454	-0.0749574	-0.2660101	-0.4092106	0.2179956
MM.A3A500	0.4885859	-0.1598076	-0.1265973	-0.2103806	0.1708103
MM.B99E00	0.3672758	-0.0798395	-0.1843762	-0.1208432	0.3755777
MM.C37EFF	0.4850096	0.1223949	-0.2840792	-0.2339267	0.3913096
MM.FB727D	0.4281761	-0.0335896	-0.2207502	-0.3633790	0.2932789
MM.B385FF	0.1743634	0.0705221	-0.1387095	-0.0326525	0.3436674
MM.D49200	0.2305061	0.2920983	-0.1482150	0.0619138	0.0675482
MM.CC9600	-0.1114272	-0.4158161	0.2247263	-0.0614412	-0.0086720
MM.DD71FA	-0.0120376	-0.3683190	0.1526563	-0.1847912	-0.0511214
MM.FF699B	0.1832787	-0.2059584	-0.2345238	-0.3807072	0.1870144
MM.AEA200	0.0364279	-0.2351406	0.0246775	-0.2377677	-0.0114991
MM.F663E2	-0.0396559	-0.3546137	0.0628408	-0.2346651	-0.0989864
MM.00BDD4	0.0199877	-0.0136117	-0.0038863	-0.0649262	-0.2838784
MM.00BD5C	0.0140797	-0.0814519	0.2181220	0.1622569	-0.1994018
MM.E28900	-0.0589930	0.0368712	0.1610854	-0.0392749	-0.2253103
MM.FF63B6	-0.2520685	-0.0531543	0.3478394	0.0626461	-0.4204959
MM.00C08C	0.1614500	-0.1082819	-0.1543919	-0.1680506	0.1756669
MM.FE6D8D	-0.0025549	-0.0425405	0.0111406	-0.0212189	-0.1031666
MM.1FB700	-0.3351864	-0.0465819	0.1693029	-0.0357561	0.0445248
MM.00B7E7	-0.0786113	0.0071915	0.0762654	0.0876867	-0.1733791
MM.00A6FF	-0.1739419	-0.2078984	0.1180385	0.1078684	-0.0914032
MM.00C0BF	-0.1973047	-0.1922326	0.1299675	-0.0144399	-0.2454681
MM.00BECA	-0.2855911	0.0260396	0.3012228	0.2956419	-0.1086428
MM.00B92F	-0.2259570	0.1128775	0.1980964	0.2720306	-0.1161659
MM.00BB48	-0.1229260	0.1052040	0.1167934	0.2665806	0.0580013
MM.00B4EF	-0.1745230	-0.2022552	0.3304780	0.1717256	-0.2292696
MM.00BADE	-0.2710436	-0.0933350	0.3498405	0.1704545	-0.4001771
MM.D177FF	-0.3147175	-0.2562477	0.3499766	-0.0150251	-0.3092823
MM.8993FF	-0.1568785	-0.3659637	0.2652017	0.0403313	-0.0740675
MM.E76BF3	-0.1639996	-0.3652531	0.2458832	0.1317597	-0.1253242
MM.DB8E00	-0.1024531	0.3704257	0.1675426	0.4143045	-0.2610028
MM.EE8045	-0.3689161	0.1718650	0.4312384	0.3901047	-0.4141214
MM.F8766D	-0.2679269	0.2957557	0.2873824	0.3340027	-0.4458381
MM.FF61C2	-0.1621256	0.3632301	0.2470977	0.4848771	-0.4595864
absGS	0.3856082	0.0330853	0.3937957	0.2418481	0.4196586
SFARI	0.0000000	0.0000000	0.0000000	0.0000000	0.0000000

rm(table_info)

original_dataset = dataset
dataset = new_dataset

rm(new_dataset)

The final dataset contains 15994 observations (genes) and 59 variables

Exploratory Analysis

PCA of Variables

The Module Membership variables are grouped by Module-Trait correlation, with positive correlations on one side, negative on the other, and both SFARI and absGS are in the middle of both groups

pca = dataset %>% mutate(SFARI = as.numeric(SFARI)) %>% t %>% prcomp

plot_data = data.frame('ID'=colnames(dataset), 'PC1' = pca$x[,1], 'PC2' = pca$x[,2],
                       type=ifelse(grepl('MM', colnames(dataset)),'ModMembership',
                            ifelse(grepl('SFARI', colnames(dataset)), 'SFARI',
                            ifelse(grepl('GS', colnames(dataset)), 'GS', 'MTcor'))))


mtcor_by_module = original_dataset %>% dplyr::select(Module, MTcor) %>% unique
colnames(mtcor_by_module) = c('ID','MTcor')

plot_data = mtcor_by_module %>% mutate(ID = gsub('#','MM.',ID)) %>% right_join(plot_data, by='ID')

ggplotly(plot_data %>% ggplot(aes(PC1, PC2, color=MTcor)) + geom_point(aes(id=ID)) +
         xlab(paste0('PC1 (',round(100*summary(pca)$importance[2,1],1),'%)')) +
         ylab(paste0('PC2 (',round(100*summary(pca)$importance[2,2],1),'%)')) +
         scale_colour_distiller(palette = 'RdBu', na.value = 'darkgrey') + theme_minimal() +
         ggtitle('PCA of variables coloured by Module-Diagnosis correlation'))

rm(mtcor_by_module, pca)

PCA of Samples

• The two main patterns that seem to characterise the genes are their Gene Significance and the Module-Diagnosis correlation of their corresponding module

• Mean Expression doesn’t seem to play an important role

• SFARI Genes seem to be evenly distributed everywhere

• It’s not clear what the 2nd principal component is capturing

# Mean Expression data
load('./../Data/preprocessed_data.RData')
datExpr = datExpr %>% data.frame
mean_expr = data.frame('ID'=rownames(datExpr), 'meanExpr' = rowMeans(datExpr))

# PCA
pca = dataset %>% t %>% prcomp

plot_data = data.frame('ID'=rownames(dataset), 'PC1'=pca$rotation[,1], 'PC2'=pca$rotation[,2], 
                       'SFARI'=dataset$SFARI, 'MTcor'=dataset$MTcor, 'GS'=dataset$GS) %>%
            mutate(alpha=ifelse(SFARI, 0.7, 0.2)) %>% left_join(mean_expr, by='ID')

p1 = plot_data %>% ggplot(aes(PC1, PC2, color=MTcor)) + geom_point(alpha=0.4) + scale_color_viridis() + 
     theme_minimal() + ggtitle('Genes coloured by Module-Diagnosis correlation') +
     xlab(paste0('PC1 (',round(100*summary(pca)$importance[2,1]),'%)')) +
     ylab(paste0('PC2 (',round(100*summary(pca)$importance[2,2]),'%)')) +
     theme(legend.position='bottom')

p2 = plot_data %>% ggplot(aes(PC1, PC2, color=GS)) + geom_point(alpha=0.4) + scale_color_viridis() + 
     xlab(paste0('PC1 (',round(100*summary(pca)$importance[2,1]),'%)')) +
     ylab(paste0('PC2 (',round(100*summary(pca)$importance[2,2]),'%)')) +
     theme_minimal() + ggtitle('Genes coloured by Gene Significance') + theme(legend.position='bottom')

p3 = plot_data %>% ggplot(aes(PC1, PC2, color=SFARI)) + geom_point(alpha = plot_data$alpha) +
     xlab(paste0('PC1 (',round(100*summary(pca)$importance[2,1]),'%)')) +
     ylab(paste0('PC2 (',round(100*summary(pca)$importance[2,2]),'%)')) +
     theme_minimal() + ggtitle('Genes coloured by SFARI label') + theme(legend.position='bottom')
p3 = ggExtra::ggMarginal(p3, type='density', groupColour=TRUE, size=10)

p4 = plot_data %>% ggplot(aes(PC1, PC2, color=meanExpr)) + geom_point(alpha=0.4) + scale_color_viridis() + 
     xlab(paste0('PC1 (',round(100*summary(pca)$importance[2,1]),'%)')) +
     ylab(paste0('PC2 (',round(100*summary(pca)$importance[2,2]),'%)')) +
     theme_minimal() + ggtitle('Genes coloured by mean level of expression') + theme(legend.position='bottom')

grid.arrange(p1, p2, p3, p4, nrow=2)

rm(pca, datExpr, datGenes, datMeta, dds, DE_info, mean_expr, p1, p2, p3, p4)

Dividing samples into Training and Test Sets

4.9% of the observations are positive. This can be a problem when training the classification model, so the samples in the training set should be balanced between classes before the model is trained.

table_info = dataset %>% apply_labels(SFARI = 'SFARI')

cro(table_info$SFARI)

	#Total
SFARI
FALSE	15211
TRUE	783
#Total cases	15994

rm(table_info)

To divide our samples into training and test sets:

• Use 75% and 25% of the samples respectively

• Even though our model’s label is binary, we are using the original SFARI Scores to do the partition in training and test sets to maintain the original score proportions in each set

set.seed(123)

sample_scores = dataset %>% mutate(ID = rownames(.)) %>% dplyr::select(ID) %>% 
                left_join(original_dataset %>% mutate(ID = rownames(.)) %>% dplyr::select(ID, gene.score), 
                          by = 'ID') %>% 
                mutate(gene.score = ifelse(is.na(gene.score), 'None', gene.score))

train_idx = createDataPartition(sample_scores$gene.score, p = 0.7, list = FALSE)
train_set = dataset[train_idx,]
test_set = dataset[-train_idx,]


# # Validation Set
# aux_dataset = dataset[-train_idx,]
# validation_idx = createDataPartition(sample_scores$gene.score[-train_idx], p = 0.5, list = FALSE)
# validation_set = aux_dataset[validation_idx,]
# 
# # Test Set
# test_set = aux_dataset[-valudation_idx,]


rm(sample_scores, train_idx)

Label distribution in training set

To fix this class imbalance, we are going to use SMOTE, an over-sampling technique that over-samples the minority class (SFARI Genes) by creating synthetic examples, in the training set

cro(train_set$SFARI)

	#Total
train_set$SFARI
FALSE	10655
TRUE	541
#Total cases	11196

Labels distribution in test set

This set is used just to evaluate how well the model performs, so the class imbalance is not a problem here

cro(test_set$SFARI)

	#Total
test_set$SFARI
FALSE	4556
TRUE	242
#Total cases	4798

Logistic Regression

Train model

# https://shiring.github.io/machine_learning/2017/04/02/unbalanced
# https://topepo.github.io/caret/using-your-own-model-in-train.html#Illustration5


train_set = train_set %>% mutate(SFARI = ifelse(SFARI==TRUE, 'SFARI', 'not_SFARI') %>% as.factor)

smote_over_sampling = trainControl(method = 'repeatedcv', number = 10, repeats = 10, verboseIter = FALSE, 
                                   classProbs = TRUE, savePredictions = 'final', 
                                   summaryFunction = twoClassSummary, sampling = 'smote')

# Using ROC as metric because it doesn't depend on the threshold
fit = caret::train(SFARI ~ ., data = train_set, method = 'glm', family = 'binomial', metric = 'ROC',
                   trControl = smote_over_sampling)

#a = caret::thresholder(fit, threshold = seq(0.05, 0.95, by = 0.1), statistics = 'all')

rm(smote_over_sampling)

Performance

The model has an AUC of 0.6805

But the features are strongly correlated, which inflates the standard error of the coefficients, making them no longer interpretable, so perhaps it would be better to use another model

# VIF
plot_data = data.frame('Feature' = car::vif(fit$finalModel) %>% sort %>% names,
                       'VIF' = car::vif(fit$finalModel) %>% sort %>% unname) %>%
            mutate(outlier = VIF>10)

plot_data %>% ggplot(aes(reorder(Feature, -VIF), VIF, fill = !outlier)) + geom_bar(stat='identity') + 
              scale_y_log10() + geom_hline(yintercept = 10, color = 'gray', linetype = 'dashed') + 
              xlab('Model Features') + ggtitle('Variance Inflation Number for each Feature') + theme_minimal() +
              theme(legend.position = 'none', axis.text.x = element_text(angle = 90, hjust = 1))

rm(plot_data)

Correlation plot

# Correlation plot
corrplot.mixed(cor(train_set[,-ncol(train_set)]), lower = 'number', lower.col = 'gray', number.cex = .6, 
               tl.pos = 'l', tl.col = '#666666')

rm(clusterings)

Possible solutions to Multicollinearity:

1. Remove all variables with a VIF>10: We would lose all but two of our variables, not ideal

2. Do Principal Component Regression: We would lose the relation between the prediction and the original features, which could be interesting to study

3. Don’t do anything: Multicollinearity affects the coefficients and p-values of the regression, but it doesn’t affect the predictions, precision of the predictions or the goodness-of-fit statistics ref, but as with the previous option, we cannot study the coefficients of the regression

4. Use Ridge Regression: The penalty it gives to high coefficients reduces the variance introduced by the correlation, making the coefficients interpretable again

Ridge Regression

Notes:

• Running the model multiple times to get more acurate measurements of its performance

• Over-sampling positive samples in the training set to obtain a 1:1 class ratio using SMOTE

• Performing 5 repetitions of cross validation with 10-folds each

### DEFINE FUNCTIONS

create_train_test_sets = function(p, seed){
  
  # Get SFARI Score of all the samples so our train and test sets are balanced for each score
  sample_scores = dataset %>% mutate(ID = rownames(.)) %>% dplyr::select(ID) %>%
                  left_join(original_dataset %>% mutate(ID = rownames(.)) %>% dplyr::select(ID, gene.score), 
                             by = 'ID') %>% 
                  mutate(gene.score = ifelse(is.na(gene.score), 'None', gene.score))

  set.seed(seed)
  train_idx = createDataPartition(sample_scores$gene.score, p = p, list = FALSE)
  
  train_set = dataset[train_idx,]
  test_set = dataset[-train_idx,]
  
  return(list('train_set' = train_set, 'test_set' = test_set))
}



run_model = function(p, seed){
  
  # Create train and test sets
  train_test_sets = create_train_test_sets(p, seed)
  train_set = train_test_sets[['train_set']]
  test_set = train_test_sets[['test_set']]
  
  # Train Model
  train_set = train_set %>% mutate(SFARI = ifelse(SFARI==TRUE, 'SFARI', 'not_SFARI') %>% as.factor)
  lambda_seq = 10^seq(1, -4, by = -.1)
  set.seed(seed)
  k_fold = 10
  cv_repeats = 5
  smote_over_sampling = trainControl(method = 'repeatedcv', number = k_fold, repeats = cv_repeats,
                                     verboseIter = FALSE, classProbs = TRUE, savePredictions = 'final', 
                                     summaryFunction = twoClassSummary, sampling = 'smote')
  fit = train(SFARI ~., data = train_set, method = 'glmnet', trControl = smote_over_sampling, metric = 'ROC',
              tuneGrid = expand.grid(alpha = 0, lambda = lambda_seq))
  
  # Predict labels in test set
  predictions = fit %>% predict(test_set, type = 'prob')
  preds = data.frame('ID' = rownames(test_set), 'prob' = predictions$SFARI) %>% mutate(pred = prob>0.5)

  # Measure performance of the model
  acc = mean(test_set$SFARI==preds$pred)
  prec = Precision(test_set$SFARI %>% as.numeric, preds$pred %>% as.numeric, positive = '1')
  rec = Recall(test_set$SFARI %>% as.numeric, preds$pred %>% as.numeric, positive = '1')
  F1 = F1_Score(test_set$SFARI %>% as.numeric, preds$pred %>% as.numeric, positive = '1')
  pred_ROCR = prediction(preds$prob, test_set$SFARI)
  AUC = performance(pred_ROCR, measure='auc')@y.values[[1]]
  
  # Extract coefficients from features
  coefs = coef(fit$finalModel, fit$bestTune$lambda) %>% as.vector
  
  return(list('acc' = acc, 'prec' = prec, 'rec' = rec, 'F1' = F1, 'AUC' = AUC, 'preds' = preds, 'coefs' =coefs))
}


### RUN MODEL

# Parameters
p = 0.75

n_iter = 25
seeds = 123:(123+n_iter-1)

# Store outputs
acc = c()
prec = c()
rec = c()
F1 = c()
AUC = c()
predictions = data.frame('ID' = rownames(dataset), 'SFARI' = dataset$SFARI, 'prob' = 0, 'pred' = 0, 'n' = 0)
coefs = data.frame('var' = c('Intercept', colnames(dataset[,-ncol(dataset)])), 'coef' = 0)

for(seed in seeds){
  
  # Run model
  model_output = run_model(p, seed)
  
  # Update outputs
  acc = c(acc, model_output[['acc']])
  prec = c(prec, model_output[['prec']])
  rec = c(rec, model_output[['rec']])
  F1 = c(F1, model_output[['F1']])
  AUC = c(AUC, model_output[['AUC']])
  preds = model_output[['preds']]
  coefs$coef = coefs$coef + model_output[['coefs']]
  update_preds = preds %>% dplyr::select(-ID) %>% mutate(n=1)
  predictions[predictions$ID %in% preds$ID, c('prob','pred','n')] = predictions[predictions$ID %in% 
                                                                      preds$ID, c('prob','pred','n')] +
                                                                    update_preds
}

coefs = coefs %>% mutate(coef = coef/n_iter)
predictions = predictions %>% mutate(prob = prob/n, pred_count = pred, pred = prob>0.5)


rm(p, seeds, update_preds, create_train_test_sets, run_model)

To summarise in a single value the predictions of the models:

• prob = Average of all the probabilities

• pred = 1 if prob>0.5, 0 otherwise

test_set = predictions %>% filter(n>0) %>% 
           left_join(dataset %>% mutate(ID = rownames(.)) %>% dplyr::select(ID, GS, MTcor), by = 'ID')
rownames(test_set) = predictions$ID[predictions$n>0]

Performance metrics

Confusion matrix

conf_mat = test_set %>% apply_labels(SFARI = 'Actual Labels', 
                                     prob = 'Assigned Probability', 
                                     pred = 'Label Prediction')

cro(conf_mat$SFARI, list(conf_mat$pred, total()))

	Label Prediction			#Total
	FALSE	TRUE
Actual Labels
FALSE	12098	3099		15197
TRUE	404	377		781
#Total cases	12502	3476		15978

rm(conf_mat)

Accuracy: Mean = 0.7791 SD = 0.0097

Precision: Mean = 0.1087 SD = 0.008

Recall: Mean = 0.4757 SD = 0.0301

F1 score: Mean = 0.1769 SD = 0.012

ROC Curve: Mean = 0.6877 SD = 0.0159

pred_ROCR = prediction(test_set$prob, test_set$SFARI)

roc_ROCR = performance(pred_ROCR, measure='tpr', x.measure='fpr')
auc = performance(pred_ROCR, measure='auc')@y.values[[1]]

plot(roc_ROCR, main=paste0('ROC curve (AUC=',round(auc,2),')'), col='#009999')
abline(a=0, b=1, col='#666666')

Lift Curve

lift_ROCR = performance(pred_ROCR, measure='lift', x.measure='rpp')
plot(lift_ROCR, main='Lift curve', col='#86b300')

rm(pred_ROCR, roc_ROCR, AUC, lift_ROCR)

---

Coefficients

MTcor and absGS have a very small coefficient and Gene Significance has a negative coefficient

gene_corr_info = dataset %>% mutate('ID' = rownames(dataset)) %>% dplyr::select(ID, MTcor, SFARI) %>% 
                 left_join(assigned_module, by ='ID') %>% mutate(Module = gsub('#','',Module))

coef_info = coefs %>% mutate('feature' = gsub('MM.','',var)) %>% 
            left_join(gene_corr_info, by = c('feature' = 'Module')) %>% 
            dplyr::select(feature, coef, MTcor, SFARI) %>% group_by(feature, coef, MTcor) %>% 
            summarise('SFARI_perc' = mean(SFARI)) %>% arrange(desc(coef))

coef_info %>% dplyr::select(feature, coef) %>% filter(feature %in% c('Intercept','GS','absGS','MTcor')) %>%
              dplyr::rename('Feature' = feature, 'Coefficient' = coef) %>% 
              kable(align = 'cc', caption = 'Regression Coefficients  (filtering MM features)') %>% 
              kable_styling(full_width = F)

Regression Coefficients (filtering MM features)

Feature	Coefficient
MTcor	-0.0396443
absGS	-0.0704092
GS	-0.1089914
Intercept	-0.5493913

• There is a positive relation between the coefficient assigned to the membership of each module and the enrichment (using ORA) in SFARI genes that are assigned to that module

load('./../Data/ORA.RData')

enrichment_SFARI_info = data.frame('Module'=as.character(), 'SFARI_enrichment'=as.numeric())
for(m in names(enrichment_SFARI)){
  m_info = enrichment_SFARI[[m]]
  enrichment = 1-ifelse('SFARI' %in% m_info$ID, m_info$pvalue[m_info$ID=='SFARI'],1)
  enrichment_SFARI_info = enrichment_SFARI_info %>% 
                          add_row(Module = gsub('#','',m), SFARI_enrichment = enrichment)
}

plot_data = coef_info %>% dplyr::rename('Module' = feature) %>% 
            left_join(enrichment_SFARI_info, by = 'Module') %>% filter(!is.na(MTcor))

ggplotly(plot_data %>% ggplot(aes(coef, SFARI_enrichment)) + 
         geom_smooth(method = 'lm', color = 'gray', alpha = 0.1) + 
         geom_point(aes(id = Module), color = paste0('#',plot_data$Module), alpha=0.7) + 
         theme_minimal() + xlab('Coefficient') + 
         ylab('SFARI Genes Enrichment'))

rm(enrichment_old_SFARI, enrichment_DGN, enrichment_DO, enrichment_GO, enrichment_KEGG, enrichment_Reactome,
   m, m_info, enrichment)

• There doesn’t seem to be a relation between the coefficient and the correlation of the module and the diagnosis.

This is not a surprise since we knew that there was a negative relation between SFARI genes and Module-Diagnosis correlation from Preprocessing/Gandal/AllRegions/RMarkdowns/20_04_03_WGCNA_modules_EA.html. The fact that there is no relation between coefficient and Module-Diagnosis correlation could even be a good sign that the model is picking some biological signal as well as the SFARI patterns (since the relation with the biological signals is positive)

ggplotly(coef_info %>% dplyr::rename('Module' = feature) %>% filter(!is.na(MTcor)) %>%
              ggplot(aes(coef, MTcor)) +  geom_smooth(method = 'lm', color = 'gray', alpha = 0.1) + 
              geom_point(aes(id = Module), color = paste0('#',coef_info$feature[!is.na(coef_info$MTcor)]), 
                         alpha = 0.7) + 
              theme_minimal() + xlab('Coefficient') + 
              ylab('Module-Diagnosis correlation'))

---

Analyse Results

Score distribution by SFARI Label

SFARI genes have a higher score distribution than the rest, but the overlap is large

plot_data = test_set %>% dplyr::select(prob, SFARI)

ggplotly(plot_data %>% ggplot(aes(prob, fill=SFARI, color=SFARI)) + geom_density(alpha=0.3) + xlab('Score') +
         geom_vline(xintercept = mean(plot_data$prob[plot_data$SFARI]), color = '#00C0C2', linetype = 'dashed') +
         geom_vline(xintercept = mean(plot_data$prob[!plot_data$SFARI]), color = '#FF7371', linetype = 'dashed') +
         theme_minimal() + ggtitle('Model score distribution by SFARI Label'))

Score distribution by SFARI Score

Even though we didn’t use the actual SFARI Scores to train the model, but instead we grouped them all together, there seems to be a statistically significant positive relation between the SFARI scores and the probability assigned by the model

plot_data = test_set %>% mutate(ID=rownames(test_set)) %>% dplyr::select(ID, prob) %>%
            left_join(original_dataset %>% mutate(ID=rownames(original_dataset)), by='ID') %>%
            mutate(gene.score = ifelse(gene.score=='Others', ifelse(ID %in% GO_neuronal$ID, 'Neuronal', 'Others'), 
                                       gene.score)) %>%
            dplyr::select(ID, prob, gene.score) %>% apply_labels(gene.score='SFARI Gene score')

cro(plot_data$gene.score)

	#Total
SFARI Gene score
1	144
2	205
3	432
Neuronal	929
Others	14268
#Total cases	15978

mean_vals = plot_data %>% group_by(gene.score) %>% summarise(mean_prob = mean(prob))

wt = plot_data %>% wilcox_test(prob~gene.score, p.adjust.method = 'BH') %>% add_x_position(x = 'group')

increase = 0.06
base = 0.9
pos_y_comparisons = c(base + c(0,1,3,5)*increase, base+c(0,2,4)*increase, base+0:1*increase, base)

plot_data %>% ggplot(aes(gene.score, prob)) + 
              geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=gene.score)) +
              stat_pvalue_manual(wt, y.position = pos_y_comparisons, tip.length = .01) +
              scale_fill_manual(values=SFARI_colour_hue(r=c(1:3,8,7))) + 
              ggtitle('Distribution of probabilities by SFARI score') +
              xlab('SFARI score') + ylab('Probability') + theme_minimal() + theme(legend.position = 'none')

rm(mean_vals, increase, base, pos_y_comparisons, wt)

Genes with the highest Probabilities

• Considering the class imbalance in the test set (1:19), there are many more SFARI scores in here (1:3)

• High concentration of genes with a SFARI Score of 1

test_set %>% dplyr::select(prob, SFARI) %>% mutate(ID = rownames(test_set)) %>% 
             arrange(desc(prob)) %>% top_n(50, wt=prob) %>%
             left_join(original_dataset %>% mutate(ID=rownames(original_dataset)), by='ID')  %>%
             mutate(gene.score = ifelse(gene.score=='Others', ifelse(ID %in% GO_neuronal$ID, 'Neuronal', 'Others'), 
                                        gene.score)) %>%
             left_join(gene_names, by = c('ID'='ensembl_gene_id')) %>%
             dplyr::rename('GeneSymbol' = external_gene_id, 'Probability' = prob, 'ModuleDiagnosis_corr' =MTcor,
                           'GeneSignificance' = GS) %>%
             mutate(ModuleDiagnosis_corr = round(ModuleDiagnosis_corr, 4), Probability = round(Probability, 4), 
                    GeneSignificance = round(GeneSignificance, 4)) %>%
             dplyr::select(GeneSymbol, GeneSignificance, ModuleDiagnosis_corr, Module, Probability,
                           gene.score) %>%
             kable(caption = 'Genes with highest model probabilities from the test set') %>% 
             kable_styling(full_width = F)

Genes with highest model probabilities from the test set

GeneSymbol	GeneSignificance	ModuleDiagnosis_corr	Module	Probability	gene.score
RPRD2	-0.0931	0.3428	#00C08C	0.8722	Others
TANC2	-0.2362	-0.5573	#00C0BF	0.8716	1
MYCBP2	-0.3960	-0.3847	#00B7E7	0.8651	Neuronal
ARID1B	0.2675	0.3428	#00C08C	0.8549	1
KMT2A	0.1343	0.6175	#44A0FF	0.8530	1
HIVEP2	0.0188	-0.9003	#00BADE	0.8529	1
ANK2	-0.4350	-0.9003	#00BADE	0.8525	1
NFAT5	0.1025	0.0721	#96A900	0.8521	Others
MBD5	0.1916	0.0721	#96A900	0.8489	1
PRDM2	-0.2516	0.0721	#96A900	0.8450	Others
EP300	-0.0235	0.3428	#00C08C	0.8446	1
RAPH1	-0.0554	-0.3847	#00B7E7	0.8406	Others
ASAP1	-0.0917	-0.3847	#00B7E7	0.8401	Others
C20orf112	0.0294	0.3428	#00C08C	0.8327	Others
FMNL1	-0.2229	-0.3847	#00B7E7	0.8310	Others
RNF111	-0.2393	0.0721	#96A900	0.8304	Others
KIAA1109	-0.1158	0.0721	#96A900	0.8303	Others
CREBBP	0.1424	0.3428	#00C08C	0.8295	1
RFX7	0.1381	0.0721	#96A900	0.8294	Others
KCNJ6	-0.1389	-0.9003	#00BADE	0.8248	Others
NRXN3	-0.6712	-0.9003	#00BADE	0.8203	1
SRRM4	-0.4423	-0.5573	#00C0BF	0.8195	Others
SAP130	-0.2497	-0.5573	#00C0BF	0.8167	Others
EP400	-0.1699	-0.5573	#00C0BF	0.8166	2
PLXNC1	-0.0093	0.5341	#EF67EB	0.8161	Others
TLE3	0.4865	0.5341	#EF67EB	0.8145	Others
RIMBP2	0.2592	0.5341	#EF67EB	0.8130	Others
AMPD3	-0.2812	-0.9003	#00BADE	0.8111	Others
HECTD4	-0.3177	-0.5573	#00C0BF	0.8107	1
SCAF4	0.0161	-0.5573	#00C0BF	0.8104	Others
DAAM1	0.1540	-0.0127	#00BF7D	0.8096	Others
TET3	0.4848	0.5341	#EF67EB	0.8095	Others
ARID1A	-0.1393	-0.5573	#00C0BF	0.8089	Others
CCRN4L	-0.0277	0.5341	#EF67EB	0.8079	Others
DROSHA	-0.4103	-0.5573	#00C0BF	0.8075	Others
HUNK	-0.3276	-0.3731	#4BB400	0.8069	Others
ANK3	-0.4808	-0.8123	#00BECA	0.8068	1
CACNG3	-0.4698	-0.6770	#00A6FF	0.8063	Others
POGZ	0.0373	0.3428	#00C08C	0.8059	1
CELF2	-0.0655	-0.9003	#00BADE	0.8053	Others
FOXJ2	0.2507	0.3428	#00C08C	0.8042	Others
TRRAP	0.3414	0.0721	#96A900	0.8033	3
SPRED2	0.2565	0.5341	#EF67EB	0.8024	Others
RNF150	0.0006	-0.0127	#00BF7D	0.8023	Others
PRICKLE1	-0.2135	-0.9003	#00BADE	0.8020	2
GATAD2B	-0.4236	-0.5573	#00C0BF	0.8018	Others
KMT2D	-0.3274	-0.5573	#00C0BF	0.8018	Others
ST8SIA3	-0.1994	-0.7233	#FF63B6	0.8013	Others
SRCAP	-0.3631	-0.5573	#00C0BF	0.8000	1
GRIN2B	-0.2787	-0.7233	#FF63B6	0.7998	1

---

Negative samples distribution

The objective of this model is to identify candidate SFARI genes. For this, we are going to focus on the negative samples (the non-SFARI genes)

negative_set = test_set %>% filter(!SFARI)

negative_set_table = negative_set %>% apply_labels(prob = 'Assigned Probability', 
                                                   pred = 'Label Prediction')

cro(negative_set_table$pred)

	#Total
Label Prediction
FALSE	12098
TRUE	3099
#Total cases	15197

3099 genes are predicted as ASD-related

negative_set %>% ggplot(aes(prob)) + geom_density(color='#F8766D', fill='#F8766D', alpha=0.5) +
                 geom_vline(xintercept=0.5, color='#333333', linetype='dotted') + xlab('Probability') +
                 ggtitle('Probability distribution of the Negative samples in the Test Set') + 
                 theme_minimal()

negative_set %>% dplyr::select(prob, SFARI) %>% mutate(ID = rownames(negative_set)) %>% 
                 arrange(desc(prob)) %>% top_n(50, wt = prob) %>%
                 left_join(original_dataset %>% mutate(ID = rownames(original_dataset)), by = 'ID')  %>%
                 mutate(gene.score = ifelse(gene.score=='Others', 
                                            ifelse(ID %in% GO_neuronal$ID,'Neuronal','Others'), 
                                            gene.score)) %>%
                 left_join(gene_names, by = c('ID'='ensembl_gene_id')) %>%
                 dplyr::rename('GeneSymbol' = external_gene_id, 'Probability' = prob, 
                               'ModuleDiagnosis_corr' =MTcor, 'GeneSignificance' = GS) %>%
                 mutate(ModuleDiagnosis_corr = round(ModuleDiagnosis_corr, 4), 
                        Probability = round(Probability, 4), 
                        GeneSignificance = round(GeneSignificance, 4)) %>%
                 dplyr::select(GeneSymbol, GeneSignificance, ModuleDiagnosis_corr, Module, Probability,
                               gene.score) %>%
                 kable(caption = 'Genes with highest model probabilities from the Negative set') %>% 
                 kable_styling(full_width = F)

Genes with highest model probabilities from the Negative set

GeneSymbol	GeneSignificance	ModuleDiagnosis_corr	Module	Probability	gene.score
RPRD2	-0.0931	0.3428	#00C08C	0.8722	Others
MYCBP2	-0.3960	-0.3847	#00B7E7	0.8651	Neuronal
NFAT5	0.1025	0.0721	#96A900	0.8521	Others
PRDM2	-0.2516	0.0721	#96A900	0.8450	Others
RAPH1	-0.0554	-0.3847	#00B7E7	0.8406	Others
ASAP1	-0.0917	-0.3847	#00B7E7	0.8401	Others
C20orf112	0.0294	0.3428	#00C08C	0.8327	Others
FMNL1	-0.2229	-0.3847	#00B7E7	0.8310	Others
RNF111	-0.2393	0.0721	#96A900	0.8304	Others
KIAA1109	-0.1158	0.0721	#96A900	0.8303	Others
RFX7	0.1381	0.0721	#96A900	0.8294	Others
KCNJ6	-0.1389	-0.9003	#00BADE	0.8248	Others
SRRM4	-0.4423	-0.5573	#00C0BF	0.8195	Others
SAP130	-0.2497	-0.5573	#00C0BF	0.8167	Others
PLXNC1	-0.0093	0.5341	#EF67EB	0.8161	Others
TLE3	0.4865	0.5341	#EF67EB	0.8145	Others
RIMBP2	0.2592	0.5341	#EF67EB	0.8130	Others
AMPD3	-0.2812	-0.9003	#00BADE	0.8111	Others
SCAF4	0.0161	-0.5573	#00C0BF	0.8104	Others
DAAM1	0.1540	-0.0127	#00BF7D	0.8096	Others
TET3	0.4848	0.5341	#EF67EB	0.8095	Others
ARID1A	-0.1393	-0.5573	#00C0BF	0.8089	Others
CCRN4L	-0.0277	0.5341	#EF67EB	0.8079	Others
DROSHA	-0.4103	-0.5573	#00C0BF	0.8075	Others
HUNK	-0.3276	-0.3731	#4BB400	0.8069	Others
CACNG3	-0.4698	-0.6770	#00A6FF	0.8063	Others
CELF2	-0.0655	-0.9003	#00BADE	0.8053	Others
FOXJ2	0.2507	0.3428	#00C08C	0.8042	Others
SPRED2	0.2565	0.5341	#EF67EB	0.8024	Others
RNF150	0.0006	-0.0127	#00BF7D	0.8023	Others
GATAD2B	-0.4236	-0.5573	#00C0BF	0.8018	Others
KMT2D	-0.3274	-0.5573	#00C0BF	0.8018	Others
ST8SIA3	-0.1994	-0.7233	#FF63B6	0.8013	Others
UBXN7	0.1028	0.3428	#00C08C	0.7990	Others
ETV6	-0.1439	-0.6508	#E76BF3	0.7968	Others
GLTSCR1L	-0.1611	0.0721	#96A900	0.7960	Others
POM121C	-0.2811	-0.5573	#00C0BF	0.7944	Others
MIDN	-0.0560	-0.5573	#00C0BF	0.7942	Others
SPTAN1	-0.2888	-0.5573	#00C0BF	0.7938	Others
BICD1	-0.0377	0.0721	#96A900	0.7918	Others
TP53INP2	-0.2133	-0.4661	#1FB700	0.7915	Others
TLN2	-0.4786	-0.7233	#FF63B6	0.7913	Others
EIF4G3	-0.4822	-0.8123	#00BECA	0.7910	Others
BAZ2A	0.0518	0.0721	#96A900	0.7895	Others
NCOA2	0.4695	0.5341	#EF67EB	0.7886	Others
RASGRF1	-0.7424	-0.9003	#00BADE	0.7881	Neuronal
UBR4	-0.2871	-0.5573	#00C0BF	0.7875	Others
SIK3	-0.1236	-0.3847	#00B7E7	0.7875	Others
TAB2	0.2276	0.0721	#96A900	0.7861	Others
FAM193A	0.0348	0.0721	#96A900	0.7853	Others

Probability and Gene Significance

• There’s a lot of noise, but the probability the model assigns to each gene seems to have a negative relation with the Gene Significance (under-expressed genes having on average the higher probabilities and over-expressed genes the lowest)

• The pattern is stronger in under-expressed genes

negative_set %>% ggplot(aes(prob, GS, color = MTcor)) + geom_point() + 
                 geom_smooth(method = 'loess', color = '#666666') +
                 geom_hline(yintercept = 0, color='gray', linetype='dashed') + 
                 xlab('Probability') + ylab('Gene Significance') +
                 scale_color_gradientn(colours=c('#F8766D','white','#00BFC4')) + 
                 ggtitle('Relation between Probability and Gene Significance') + theme_minimal()

Probability and Module-Diagnosis correlation

• There’s not a strong relation between the Module-Diagnosis correlation of the genes assigned module and the probability assigned by the model

• The model seems to assign slightly higher probabilities to genes belonging the modules with negative module-Dianosis correlations than to genes belonging to modules with positive ones

negative_set %>% ggplot(aes(MTcor, prob, color=GS)) + geom_point() + 
                 geom_smooth(method='loess', color='#666666') + 
                 geom_hline(yintercept=mean(negative_set$prob), color='gray', linetype='dashed') +
                 scale_color_gradientn(colours=c('#F8766D','#F8766D','white','#00BFC4','#00BFC4')) + 
                 xlab('Modules ordered by their correlation to ASD') + ylab('Model probability') +
                 theme_minimal()

Summarised version, plotting by module instead of by gene

The difference in the trend lines between this plot and the one above is that the one above takes all the points into consideration while this considers each module as an observation by itself, so the top one is strongly affected by big modules and the bottom one treats all modules the same

The model seems to give lower probabilities to genes belonging to modules with a high (absolute) correlation to Diagnosis (this is unexpected)

plot_data = negative_set %>% group_by(MTcor) %>% summarise(mean = mean(prob), sd = sd(prob), n = n()) %>%
            mutate(MTcor_sign = ifelse(MTcor>0, 'Positive', 'Negative')) %>% 
            left_join(original_dataset, by='MTcor') %>%
            dplyr::select(Module, MTcor, MTcor_sign, mean, sd, n) %>% distinct()
colnames(plot_data)[1] = 'ID'

ggplotly(plot_data %>% ggplot(aes(MTcor, mean, size=n, color=MTcor_sign)) + geom_point(aes(id=ID), alpha=0.7) + 
         geom_smooth(method='loess', color='gray', se=FALSE) + geom_smooth(method='lm', se=FALSE) + 
         xlab('Module-Diagnosis correlation') + ylab('Mean Probability by Module') + theme_minimal())

Probability and level of expression

There is a positive relation between level of expression and probability, the model seems to be capturing indirectly the level of expression of the genes to make the prediction, so it’s introducing the same bias

We had already found some mean expression bias in the SFARI scores, but thought we had eliminated this information when constructing the WGCNA network (because correlation is independent to linear transformations), but a signal related to this seems to have survived into the network and the model is picking it up

# Gandal dataset
load('./../Data/preprocessed_data.RData')
datExpr = datExpr %>% data.frame
DE_info = DE_info %>% data.frame

mean_and_sd = data.frame(ID=rownames(datExpr), meanExpr=rowMeans(datExpr), sdExpr=apply(datExpr,1,sd))

plot_data = negative_set %>% left_join(mean_and_sd, by='ID') %>% 
            left_join(original_dataset %>% mutate(ID=rownames(original_dataset)) %>% 
                      dplyr::select(ID, Module), by='ID')
colnames(plot_data)[ncol(plot_data)] = 'Module'

plot_data %>% ggplot(aes(meanExpr, prob)) + geom_point(alpha=0.2, color='#0099cc') + 
              geom_smooth(method='loess', color='gray', alpha=0.2) + xlab('Mean Expression') + 
              ylab('Probability') + ggtitle('Mean expression vs model probability by gene') +
              theme_minimal()

rm(mean_and_sd)

plot_data2 = plot_data %>% group_by(Module) %>% summarise(meanExpr = mean(meanExpr), meanProb = mean(prob), 
                                                          n=n())

ggplotly(plot_data2 %>% ggplot(aes(meanExpr, meanProb, size=n)) + 
         geom_point(color=plot_data2$Module, alpha = 0.6) + 
         geom_smooth(method='loess', se=TRUE, color='gray', alpha=0.1, size=0.7) + 
         xlab('Mean Level of Expression') + ylab('Average Model Probability') +
         theme_minimal() + theme(legend.position='none') + 
         ggtitle('Mean expression vs model probability by Module'))

rm(plot_data2)

Probability and LFC

There is a relation between probability and LFC, so it IS capturing a bit of true information (because LFC and mean expression were negatively correlated and it still has a positive relation in the model)

• The relation is stronger in genes under-expressed in ASD

plot_data = negative_set %>% left_join(DE_info %>% mutate(ID=rownames(DE_info)), by='ID')

plot_data %>% ggplot(aes(log2FoldChange, prob)) + geom_point(alpha=0.1, color='#0099cc') + 
              geom_smooth(method='loess', color='gray', alpha=0.2) + xlab('LFC') +
              xlab('LFC') + ylab('Probability') +
              theme_minimal() + ggtitle('LFC vs model probability by gene')

• The relation is stronger in Differentially Expressed genes

p1 = plot_data %>% filter(log2FoldChange<0) %>% mutate(DE = padj<0.05) %>%
     ggplot(aes(log2FoldChange, prob, color=DE)) + geom_point(alpha=0.1) + 
     geom_smooth(method='loess', alpha=0.1) + xlab('') + ylab('Probability') + 
     ylim(c(min(plot_data$prob), max(plot_data$prob))) + 
     theme_minimal() + theme(legend.position = 'none', plot.margin=unit(c(1,-0.3,1,1), 'cm'))

p2 = plot_data %>% filter(log2FoldChange>=0) %>% mutate(DE = padj<0.05) %>% 
     ggplot(aes(log2FoldChange, prob, color=DE)) + geom_point(alpha=0.1) + 
     geom_smooth(method='loess', alpha=0.1) + xlab('') + ylab('Probability') + ylab('') +
     scale_y_continuous(position = 'right', limits = c(min(plot_data$prob), max(plot_data$prob))) +
     theme_minimal() + theme(plot.margin = unit(c(1,1,1,-0.3), 'cm'), axis.ticks.y = element_blank())

grid.arrange(p1, p2, nrow=1, top = 'LFC vs model probability by gene', bottom = 'LFC')

rm(p1, p2)

Conclusion

The model is capturing the mean level of expression of the genes (indirectly through module memberhsip), which is a strong bias found in the SFARI scores, but it seems to be capturing a bit of true biological signal as well (based on the GS and the log fold change plots)

---

Saving results

predictions = test_set %>% left_join(gene_names, by = c('ID' = 'ensembl_gene_id'))

save(predictions, dataset, fit, file='./../Data/Ridge_model.RData')

---

Session info

sessionInfo()

## R version 3.6.3 (2020-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.4 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
## 
## locale:
##  [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8    
##  [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
##  [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] grid      parallel  stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] DMwR_0.4.1         doParallel_1.0.15  iterators_1.0.12   foreach_1.5.0     
##  [5] kableExtra_1.1.0   expss_0.10.2       corrplot_0.84      MLmetrics_1.1.1   
##  [9] car_3.0-7          carData_3.0-3      ROCR_1.0-7         gplots_3.0.3      
## [13] caret_6.0-86       lattice_0.20-41    polycor_0.7-10     biomaRt_2.40.5    
## [17] rstatix_0.6.0      ggpubr_0.2.5       magrittr_1.5       RColorBrewer_1.1-2
## [21] gridExtra_2.3      viridis_0.5.1      viridisLite_0.3.0  plotly_4.9.2      
## [25] knitr_1.28         forcats_0.5.0      stringr_1.4.0      dplyr_1.0.0       
## [29] purrr_0.3.4        readr_1.3.1        tidyr_1.1.0        tibble_3.0.1      
## [33] ggplot2_3.3.2      tidyverse_1.3.0   
## 
## loaded via a namespace (and not attached):
##   [1] tidyselect_1.1.0            RSQLite_2.2.0              
##   [3] AnnotationDbi_1.46.1        htmlwidgets_1.5.1          
##   [5] BiocParallel_1.18.1         pROC_1.16.2                
##   [7] munsell_0.5.0               codetools_0.2-16           
##   [9] miniUI_0.1.1.1              withr_2.2.0                
##  [11] colorspace_1.4-1            Biobase_2.44.0             
##  [13] highr_0.8                   rstudioapi_0.11            
##  [15] stats4_3.6.3                ggsignif_0.6.0             
##  [17] TTR_0.23-6                  labeling_0.3               
##  [19] GenomeInfoDbData_1.2.1      bit64_0.9-7                
##  [21] farver_2.0.3                vctrs_0.3.1                
##  [23] generics_0.0.2              ipred_0.9-9                
##  [25] xfun_0.12                   R6_2.4.1                   
##  [27] GenomeInfoDb_1.20.0         locfit_1.5-9.4             
##  [29] bitops_1.0-6                DelayedArray_0.10.0        
##  [31] assertthat_0.2.1            promises_1.1.0             
##  [33] scales_1.1.1                nnet_7.3-14                
##  [35] ggExtra_0.9                 gtable_0.3.0               
##  [37] timeDate_3043.102           rlang_0.4.6                
##  [39] genefilter_1.66.0           splines_3.6.3              
##  [41] lazyeval_0.2.2              ModelMetrics_1.2.2.2       
##  [43] acepack_1.4.1               broom_0.7.0                
##  [45] checkmate_2.0.0             yaml_2.2.1                 
##  [47] reshape2_1.4.4              abind_1.4-5                
##  [49] modelr_0.1.6                crosstalk_1.1.0.1          
##  [51] backports_1.1.8             quantmod_0.4.17            
##  [53] httpuv_1.5.2                Hmisc_4.4-0                
##  [55] tools_3.6.3                 lava_1.6.7                 
##  [57] ellipsis_0.3.1              BiocGenerics_0.30.0        
##  [59] Rcpp_1.0.4.6                plyr_1.8.6                 
##  [61] base64enc_0.1-3             progress_1.2.2             
##  [63] zlibbioc_1.30.0             RCurl_1.98-1.2             
##  [65] prettyunits_1.1.1           rpart_4.1-15               
##  [67] zoo_1.8-8                   S4Vectors_0.22.1           
##  [69] SummarizedExperiment_1.14.1 haven_2.2.0                
##  [71] cluster_2.1.0               fs_1.4.0                   
##  [73] data.table_1.12.8           openxlsx_4.1.4             
##  [75] reprex_0.3.0                matrixStats_0.56.0         
##  [77] mime_0.9                    hms_0.5.3                  
##  [79] evaluate_0.14               xtable_1.8-4               
##  [81] XML_3.99-0.3                rio_0.5.16                 
##  [83] jpeg_0.1-8.1                readxl_1.3.1               
##  [85] shape_1.4.4                 IRanges_2.18.3             
##  [87] compiler_3.6.3              KernSmooth_2.23-17         
##  [89] crayon_1.3.4                htmltools_0.4.0            
##  [91] mgcv_1.8-31                 later_1.0.0                
##  [93] Formula_1.2-3               geneplotter_1.62.0         
##  [95] lubridate_1.7.4             DBI_1.1.0                  
##  [97] dbplyr_1.4.2                MASS_7.3-51.6              
##  [99] Matrix_1.2-18               cli_2.0.2                  
## [101] gdata_2.18.0                gower_0.2.1                
## [103] GenomicRanges_1.36.1        pkgconfig_2.0.3            
## [105] foreign_0.8-76              recipes_0.1.10             
## [107] xml2_1.2.5                  annotate_1.62.0            
## [109] webshot_0.5.2               XVector_0.24.0             
## [111] prodlim_2019.11.13          rvest_0.3.5                
## [113] digest_0.6.25               rmarkdown_2.1              
## [115] cellranger_1.1.0            htmlTable_1.13.3           
## [117] curl_4.3                    shiny_1.4.0.2              
## [119] gtools_3.8.2                lifecycle_0.2.0            
## [121] nlme_3.1-147                jsonlite_1.7.0             
## [123] fansi_0.4.1                 pillar_1.4.4               
## [125] fastmap_1.0.1               httr_1.4.1                 
## [127] survival_3.1-12             xts_0.12-0                 
## [129] glue_1.4.1                  zip_2.0.4                  
## [131] png_0.1-7                   glmnet_3.0-2               
## [133] bit_1.1-15.2                class_7.3-17               
## [135] stringi_1.4.6               blob_1.2.1                 
## [137] DESeq2_1.24.0               latticeExtra_0.6-29        
## [139] caTools_1.18.0              memoise_1.1.0